Characterizing tissue species using nuclear magnetic resonance (“NMR”) can include selecting a particular property of a resonant species (e.g., T1 spin-lattice relaxation, T2 spin-spin relaxation, proton density) and then assessing anatomical images having a weighting toward the selected property to make clinical assessments of the tissue. In this way, assessments are qualitative and limited to the information provided by the particular weighting of the data/image. Magnetic resonance fingerprinting (“MRF”), which is described, as one example, by D. Ma, et al., in “Magnetic Resonance Fingerprinting,” Nature, 2013; 495(7440):187-192, breaks from the traditional paradigm of NMR or magnetic resonance imaging (“MRI”) to acquire data from a resonant species across a variety of properties and provides a framework to quantitatively assess the data.
Conventional MRI pulse sequences include repetitive similar preparation phases, waiting phases, and acquisition phases that serially produce signals from which images can be made. The preparation phase determines when a signal can be acquired and determines the properties of the acquired signal. For example, a first pulse sequence may produce a T1-weighted signal at a first echo time (“TE”), while a second pulse sequence may produce a T2-weighted signal at a second TE. These conventional pulse sequences typically provide qualitative results where data are acquired with various weightings or contrasts that highlight a particular parameter (e.g., T1 relaxation, T2 relaxation).
When magnetic resonance (“MR”) images are generated, they may be viewed by a radiologist and/or surgeon who interprets the qualitative images for specific disease signatures. The radiologist may examine multiple image types (e.g., T1-weighted, T2-weighted) acquired in multiple imaging planes to make a diagnosis. The radiologist or other individual examining the qualitative images may need particular skill to be able to assess changes from session to session, from machine to machine, and from machine configuration to machine configuration.
Unlike conventional MRI, MRF employs a series of varied sequence blocks that simultaneously produce different signal evolutions in different resonant species (e.g., tissues) to which the radio frequency (“RF”) is applied. The signals from different resonant tissues will, however, be different and can be distinguished using MRF. The different signals can be collected over a period of time to identify a signal evolution for the volume. Resonant species in the volume can then be characterized by comparing the signal evolution to known evolutions. Characterizing the resonant species may include identifying a material or tissue type, or may include identifying MR parameters associated with the resonant species. The “known” evolutions may be, for example, simulated evolutions calculated from physical principles and/or previously acquired evolutions. A large set of known evolutions may be stored in a dictionary.
Existing MRF techniques acquire a series of images by using random, pseudo-random, or otherwise varied acquisition parameters, instead of a fixed set of parameters as used in traditional MR imaging. The goal is to elicit different signal evolutions from each type of tissue so that the time-series signal at each image voxel has a unique representation that can be compared to a pre-calculated dictionary containing many or all expected signals. In certain MRF implementations, flip angle (FA) and repetition time (TR) varies from frame to frame to drive the signal in transient state that is highly sensitive to the relaxation parameters. Readout encoding gradients can also be varied to generate a spatial temporal incoherence that helps “see through” the highly aliased images by the template matching algorithm.
The volume (a slice in 2D method or a slab in 3D acquisition) excited by the RF pulse stays constant in MRF acquisition schemes. In-plane spatial resolution is determined by the maximum value of k-space that a readout-encoding gradient can generate. In order to achieve higher resolution, a larger k-space value must be generated, which generally requires a longer gradient readout because the gradient strength is limited in clinical MR scanners. This longer gradient readout can lead to geometrical distortion with echo planar trajectories, or severely blurred images with spiral trajectories.
Therefore, it would be desirable to provide new systems and methods for MRF that achieve greater resolution, without sacrificing overall duration of the acquisition or inducing undesired artifacts in the resulting images.